Experimental study on nanosecond pulsed pin-to-plate discharge in supersonic air flow

Development of magnetohydrodynamic acceleration technology is expected to improve wind tunnel simulation capability and testing capability.The underlying premise is to produce uniform and stable plasma in supersonic air flow,and gas discharge is an effective way to achieve this.A nanosecond pulsed discharge experimental system under supersonic conditions was established,and a pin-to-plate nanosecond pulsed discharge experiment in Mach 2 air flow was performed to verify that the proposed method produced uniform and stable plasma under supersonic conditions.The results show that the discharge under supersonic conditions was stable overall,but uniformity was not as good as that under static conditions.Increasing the number of pins improved discharge uniformity,but reduced discharge intensity and hence plasma density.Under multi-pin conditions at 1000Hz,the discharge was almost completely corona discharge,with the main current component being the displacement current,which was smaller than that under static conditions.

Adaptive immune responses to SARS-CoV-2 infection in severe versus mild individuals

The global Coronavirus disease 2019(COVID-19)pandemic caused by SARS-CoV-2 has affected more than eight million people.There is an urgent need to investigate how the adaptive immunity is established in COVID-19 patients.In this study,we proled adaptive immune cells of PBMCs from recovered COVID-19 patients with varying disease severity using single-cell RNA and TCR/BCR V(D)J sequencing.The sequencing data revealed SARS-CoV-2-specic shufing of adaptive immune repertories and COVID-19-induced remodeling of peripheral lymphocytes.Characterization of variations in the peripheral T and B cells from the COVID-19 patients revealed a positive correlation of humoral immune response and T-cell immune memory with disease severity.Sequencing and functional data revealed SARS-CoV-2-specic T-cell immune memory in the convalescent COVID-19 patients.Furthermore,we also identied novel antigens that are responsive in the convalescent patients.Altogether,our study reveals adaptive immune repertories underlying pathogenesis and recovery in severe versus mild COVID-19 patients,providing valuable information for potential vaccine and therapeutic development against SARS-CoV-2 infection.

Hybrid model of self-organizing map and kernel auto-associator for internet intrusion detection

Purpose–The task of internet intrusion detection is to detect anomalous network connections caused by intrusive activities.There have been many intrusion detection schemes proposed,most of which apply both normal and intrusion data to construct classifiers.However,normal data and intrusion data are often seriously imbalanced because intrusive connection data are usually difficult to collect.Internet intrusion detection can be considered as a novelty detection problem,which is the identification of new or unknown data,to which a learning system has not been exposed during training.This paper aims to address this issue.Design/methodology/approach–In this paper,a novelty detection-based intrusion detection system is proposed by combining the self-organizing map(SOM)and the kernel auto-associator(KAA)model proposed earlier by the first author.The KAA model is a generalization of auto-associative networks by training to recall the inputs through kernel subspace.For anomaly detection,the SOM organizes the prototypes of samples while the KAA provides data description for the normal connection patterns.The hybrid SOM/KAA model can also be applied to classify different types of attacks.Findings–Using the KDD CUP,1999 dataset,the performance of the proposed scheme in separating normal connection patterns from intrusive connection patterns was compared with some state-of-art novelty detection methods,showing marked improvements in terms of the high intrusion detection accuracy and low false positives.Simulations on the classification of attack categories also demonstrate favorable results of the accuracy,which are comparable to the entries from the KDD CUP,1999 data mining competition.Originality/value–The hybrid model of SOM and the KAA model can achieve significant results for intrusion detection.

Vehicle identification by improved stacking via kernel principal component regression

Purpose–Many applications in intelligent transportation demand accurate categorization of vehicles.The purpose of this paper is to propose a working image-based vehicle classification system.The first component vehicle detection is implemented by applying Dalal and Triggs’s histograms of oriented gradients features and linear support vector machine(SVM)classifier.The second component vehicle classification,which is the emphasis of this paper,is accomplished by an improved stacked generalization.As an effective ensemble learning strategy,stacked generalization has been proposed to combine multiple models using the concept of a meta-learner.However,it was found that the well-known meta-learning scheme multi-response linear regression(MLR)for stacked generalization performs poorly on the vehicle classification.Design/methodology/approach–A new meta-learner is then proposed based on kernel principal component regression(KPCR).The stacked generalization scheme consists of a heterogeneous classifier ensemble with seven base classifiers,i.e.linear discriminant classifier,fuzzy k-nearest neighbor,logistic regression,Parzen classifier,Gaussian mixture model,multiple layer perceptron and SVM.Findings–Experimental results using more than 2,500 images from four types of vehicles(bus,light truck,car and van)demonstrated the effectiveness of the proposed approach.The improved stacked generalization produced consistently better results when compared to any of the single base classifier used and four other beta learning algorithms,including MLR,majority voting,logistic regression and decision template.Originality/value–With the seven base classifiers,the KPCR-based stacking offers a performance of 96 percent accuracy and 95 percent k coefficient,thus exhibiting promising potentials for real-world applications.